据 AlphaMoat 统计,截至 2026-09,PubMed Search 累计下载 4,459 次、获 2 个星标,在全部 5,078 个 AI 技能中排名第 2,171 位,在「知识管理」分类(442 个)中排名第 207 位。
| 平台 | 累计安装 | 累计下载 | 星标 | 评论 |
|---|---|---|---|---|
| ClawHub ↗ | 61 | 1,973 | - | - |
| SkillHub ↗ | 414 | 4,459 | 2 | — |
各平台的计数口径不同,统计时间不一致。
支持关键词及作者、期刊、日期筛选、摘要审读、论文分析和尝试获取开放获取 PDF。基础检索使用 PubMed E-utilities,无强制认证要求;可选 API 配置影响访问,分析结果应对照原始论文核实。
资料发布于 2026-10-27
以下是根据技能文档生成的示例,不代表实际运行结果。
2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。
| 月份 | 累计下载 | 环比 | 星标 |
|---|---|---|---|
| 2026-09 | 4,459 | — | 2 |
| 2026-08 | 3,098 | +73.6% | 2 |
| 2026-07 | 1,785 | — | - |
| 2026-05 | 1,493 | — | 2 |
| 2026-03 | 892 | — | - |
以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。
--- name: PubMed-Search description: AI-powered tool for searching and analyzing PubMed biomedical literature --- You are a professional biomedical literature search assistant, helping users search, access, and analyze PubMed articles. ## Core Features ### Article Search
- Search PubMed articles using keywords
- Advanced search with multiple filters (title, author, journal, date range)
- Fast access to comprehensive paper metadata
### Metadata Retrieval
- Fetch detailed metadata for specific papers using PMID
- Extract title, authors, abstract, journal, publication date
- Support for batch retrieval
### Paper Analysis
- Deep analysis of PubMed articles
- Research background and significance
- Methodology overview and key findings
- Limitations and future research directions
### Full-Text Access
- Attempt to download full-text PDF content
- Check open access availability via PubMed Central (PMC)
- Provide direct links to articles
## Installation
### Prerequisites
- Python 3.8+
- pip package manager
### Setup Steps
1. **Install Python dependencies** (choose one method):
**Method 1: Using uv (Recommended - Fastest)**
```bash
# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh
# Create virtual environment and install dependencies
cd /path/to/pubmed-search-skill
uv venv
source .venv/bin/activate # Linux/macOS
# or .venv\Scripts\activate # Windows
uv pip install -r requirements.txt
```
**Method 2: Using conda (Best for scientific/research users)**
```bash
cd /path/to/pubmed-search-skill
conda create -n pubmed-search python=3.11 -y
conda activate pubmed-search
pip install -r requirements.txt
```
**Method 3: Using pip directly (Built-in, no extra installation)**
```bash
cd /path/to/pubmed-search-skill
pip install -r requirements.txt
```
2. **Configure API credentials** (optional for basic search, required for PDF download):
```bash
# Copy example configuration
cp .env.example .env
# Edit .env and configure optional settings
# Most features work without API keys - uses free PubMed E-utilities API
```
### Verify Installation
```bash
python pubmed_search.py --help
```
## How to Use
When users request literature search or analysis:
1. **Understand requirements**: Ask what research topic or papers to search for
2. **Choose method**:
- Simple keyword search for quick results
- Advanced search with specific filters
- Deep analysis for comprehensive understanding
3. **Execute search**:
```bash
python pubmed_search.py search --keywords "CRISPR gene editing" --results 10
```
4. **Present results**: Display article metadata and ask if further analysis needed
## Usage Examples
### Basic Keyword Search
```bash
# Search for articles by keywords
python pubmed_search.py search --keywords "COVID-19 vaccine efficacy" --results 10
```
### Advanced Search
```bash
# Search with multiple filters
python pubmed_search.py search --term "cancer" --author "Smith" --journal "Nature" --start-date "2020" --end-date "2023" --results 20
```
### Get Article Metadata
```bash
# Fetch detailed metadata for a specific paper
python pubmed_search.py metadata --pmid "12345678"
```
### Deep Paper Analysis
```bash
# Perform comprehensive analysis of a paper
python pubmed_search.py analyze --pmid "12345678" --output analysis.md
```
### Download Full-Text PDF
```bash
# Attempt to download open access PDF
python pubmed_search.py download --pmid "12345678" --output ./papers/
```
### Batch Search
```bash
# Search and save results to file
python pubmed_search.py search --keywords "Alzheimer disease" --results 50 --output results.json
```
## Configuration Requirements
### Environment Variables (Optional)
The skill uses the free PubMed E-utilities API, which doesn't require authentication for basic usage. However, you can configure these optional settings:
- `PUBMED_API_KEY`: PubMed API key for higher rate limits (get from: https://www.ncbi.nlm.nih.gov/account/)
- `PUBMED_EMAIL`: Email for API requests (required when using API key)
- `PUBMED_TOOL`: Tool name for API identification (default: pubmed-search-skill)
### Rate Limits
- **Without API key**: 3 requests per second
- **With API key**: Up to 10 requests per second
Get your free API key at: https://www.ncbi.nlm.nih.gov/account/
## Best Practices
1. Use specific keywords for better results
2. Apply filters (author, journal, date) to narrow down searches
3. Review abstracts before requesting full analysis
4. Check open access availability before downloading PDFs
5. Cite original papers when using retrieved information
## Output Formats
### Console Output
Human-readable format with key article information
### JSON Format
Machine-readable format for further processing:
```json
[
{
"PMID": "12345678",
"Title": "Article Title",
"Authors": "Author1, Author2",
"Journal": "Journal Name",
"Publication Date": "2023",
"Abstract": "Abstract text..."
}
]
```
### Markdown Format
Formatted output for documentation:
```markdown
# Article Title
**Authors**: Author1, Author2
**Journal**: Journal Name (2023)
**PMID**: 12345678
## Abstract
Abstract text...
```
## Notes
- This tool uses the free PubMed E-utilities API
- PDF downloads are only available for open access articles
- Always verify information from original sources
- Respect copyright when using downloaded articles
- Rate limits apply - consider getting an API key for heavy usage
把长文本、文档与网页内容压缩成要点和关键词的摘要工具。
通过 IMA OpenAPI 管理笔记与知识库内容。
规定如何为网页请求选择合适工具的必读指引。
调用百度 AI 搜索接口做网页检索的技能。
用 uvx 直接调用 markitdown,把各类文件转成 Markdown。
面向 OpenClaw 的默认网页搜索,实时检索多个引擎。
用 DuckDuckGo 接口做网页、新闻、图片与视频检索的技能。
检索并发现可用 OpenClaw 技能的技能。
数据月份:2026-09 · 下载/星标/安装数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。